A Co-evolutionary Differential Evolution Algorithm for Constrained Optimization
Bo Liu, Hannan Ma, Xuejun Zhang · 2007
In this paper, a co-evolutionary differential evolution algorithm (CODE) for constrained optimization is proposed. Two cooperative populations are constructed and evolved by independent differential evolution (DE) algorithm. The purpose of the first population is to minimize the objective function regardless of constraints, and that of the second population is to minimize the violation of constraints regardless of the objective function. Interaction and migration happens between the two populations when separate evolutions go on several generations, by migrating feasible solutions into the first group, and infeasible ones into the second group. The algorithm is tested by five famous benchmark problems, and is compared with methods based on penalty functions and cooperative co-evolutionary genetic algorithm. The results proved the proposed cooperative CODE is very effective and efficient.